name	model-serving
description	Serve models reliably: choose online vs batch by the use case, deploy a versioned model from the registry, optimize latency to a budget (batching/quantization/distillation/hardware), and roll out safely with shadow -> canary -> full.

Model Serving

Real-time per request -> online endpoint. Periodic scoring of many records -> batch. Don't mismatch.

Deploy a specific registered version with known lineage — not a copied file. Promotion goes through the registry gate.

Batching, quantization/distillation, right hardware, caching — to a budget. Measure.

Shadow (compare on live traffic, no user impact) -> canary (a slice) -> full, promote on the metric. Blind swaps are silent regressions.

name	model-serving
description	Serve models reliably: choose online vs batch by the use case, deploy a versioned model from the registry, optimize latency to a budget (batching/quantization/distillation/hardware), and roll out safely with shadow -> canary -> full.

Real-time per request -> online endpoint. Periodic scoring of many records -> batch. Don't mismatch.

Deploy a specific registered version with known lineage — not a copied file. Promotion goes through the registry gate.

Batching, quantization/distillation, right hardware, caching — to a budget. Measure.

Shadow (compare on live traffic, no user impact) -> canary (a slice) -> full, promote on the metric. Blind swaps are silent regressions.

model-serving